PhD Pharmacologist or Toxicologist (Drug Discovery), AI Expert
New
W
Weekday AIDrug discovery
Eligible Locations: United Kingdom or US East Coast.ContractMiddle
Salary70 - 100 USD per hour
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Job Details
- Experience
- At least 3 years of experience in early- to mid-stage drug discovery, pharmacology, DMPK/ADME, or toxicology within a recognized pharmaceutical or biotechnology company, contract research organization (CRO), or academic drug discovery group.
Requirements
- PhD in Pharmacology, Toxicology, Pharmaceutical Sciences, DMPK, Biochemistry, or a closely related discipline; equivalent research experience may also be considered.
- At least 3 years of experience in early- to mid-stage drug discovery, pharmacology, DMPK/ADME, or toxicology in a pharmaceutical or biotechnology company, CRO, or academic drug discovery group.
- Hands-on experience analyzing experimental drug discovery data, including one or more of in-vitro ADME and safety assay results, PK/PD studies, CRO-generated assay data, or LC-MS-based bioanalytical data.
- Ability to interpret experimental findings and use scientific evidence to guide compound selection, safety assessment, and optimization decisions.
- Familiarity with PK/PD or data analysis software and scientific AI tools; programming expertise is not required.
- Strong analytical and problem-solving abilities and understanding of drug discovery workflows and experimental methodologies.
- Strong written communication skills, including the ability to explain complex scientific concepts and technical decisions clearly.
- Ability to commit reliably to at least 20 hours per week on weekdays.
- Applicants must be based in the United Kingdom or on the US East Coast.
Responsibilities
- Identify scientific knowledge gaps and help improve model performance across drug discovery, pharmacology, toxicology, ADME, and PK/PD.
- Design domain-specific drug discovery tasks, benchmarks, and training datasets that assess scientific reasoning.
- Develop structured solutions to drug discovery problems, including interpreting assay results, identifying compound liabilities, and planning optimization strategies.
- Evaluate AI-generated outputs and reasoning for errors in scientific logic, factual accuracy, and use of scientific tools.
- Provide written feedback to improve the reliability and scientific quality of AI-generated responses.
- Develop task specifications, grading rubrics, and verification processes for drug discovery data produced by external technical teams.
- Assess assay design and quality control, identify experimental failure modes, and validate the relevance of AI-generated recommendations.
- Review experimental findings and scientific evidence to inform compound selection and optimization.
- Collaborate with subject-matter experts to maintain consistency and scientific rigor across AI training datasets.
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